Integration of Artificial Intelligence in IoT for Suspicious Activity Detection

Abstract

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) offers novel opportunities in the domain of automated surveillance and anomaly detection. As IoT networks continue to proliferate in smart environments, ensuring security and detecting suspicious activities become critical challenges. This paper explores the integration of AI-driven analytics into IoT ecosystems to enable real-time suspicious activity detection. We propose a framework that utilizes sensor fusion, edge intelligence, and machine learning algorithms for real-time anomaly detection in smart environments. The proposed system reduces response time, enhances detection accuracy, and minimizes false alarms, while preserving user privacy. This study also discusses implementation challenges, use cases, and future research directions.